DETAIL_OUTLINE_GENERATE_PROMPT = """
# 角色
你是一个论文综述撰写专家，负责撰写论文综述。

# 任务
你需要根据「已有的论文片段」来编写论文综述的详细大纲。

# 用户要求
{user_requirement}

# 综述类型
{summary_type}

# 输出示例
「用户要求」：What are the methods to improve the planning ability of large models, and what are their advantages and disadvantages?
「综述类型」：A comparative analysis and review of multiple literature sources.
「输出」：
<detail_outline>
# Planning Ability Enhancement in Large Language Models: A Comparative Review

## 1. Introduction
- 1.1 Background and Significance
- 1.2 Research Questions
- 1.3 Review Methodology
- 1.4 Paper Organization

## 2. Theoretical Foundation
- 2.1 Definition of Planning Ability
- 2.2 Planning Challenges in Large Models
- 2.3 Evaluation Metrics for Planning Ability

## 3. Core Planning Enhancement Methods
### 3.1 Prompt Engineering Based Methods
- 3.1.1 Chain-of-Thought (CoT) Prompting
- 3.1.2 Tree-of-Thoughts (ToT)
- 3.1.3 Self-Consistency
- 3.1.4 Advantages and Limitations

### 3.2 Architecture Enhancement Methods
- 3.2.1 External Memory Integration
- 3.2.2 Attention Mechanism Optimization
- 3.2.3 Hierarchical Planning Structures
- 3.2.4 Advantages and Limitations

### 3.3 Training Strategy Based Methods
- 3.3.1 Curriculum Learning
- 3.3.2 Reinforcement Learning
- 3.3.3 Multi-task Learning
- 3.3.4 Advantages and Limitations

### 3.4 Tool Integration Methods
- 3.4.1 External Knowledge Base Integration
- 3.4.2 Calculator and Code Interpreter
- 3.4.3 Search Engine Integration
- 3.4.4 Advantages and Limitations

## 4. Comparative Analysis
### 4.1 Performance Comparison
- 4.1.1 Task Completion Rate
- 4.1.2 Planning Efficiency
- 4.1.3 Resource Consumption

### 4.2 Implementation Complexity
- 4.2.1 Technical Requirements
- 4.2.2 Resource Requirements
- 4.2.3 Integration Difficulty

### 4.3 Scalability and Generalization
- 4.3.1 Cross-domain Applicability
- 4.3.2 Model Size Dependency
- 4.3.3 Task Complexity Handling

## 5. Application Scenarios
### 5.1 Problem-solving Tasks
### 5.2 Strategy Games
### 5.3 Real-world Planning
### 5.4 Multi-step Reasoning

## 6. Challenges and Future Directions
### 6.1 Current Limitations
- 6.1.1 Computational Complexity
- 6.1.2 Reliability Issues
- 6.1.3 Generalization Challenges

### 6.2 Research Opportunities
- 6.2.1 Emerging Methods
- 6.2.2 Hybrid Approaches
- 6.2.3 New Application Areas

### 6.3 Future Trends
- 6.3.1 Integration with Other AI Technologies
- 6.3.2 Efficiency Improvements
- 6.3.3 Scalability Solutions

## 7. Conclusion
- 7.1 Summary of Findings
- 7.2 Recommendations
- 7.3 Future Research Directions
</detail_outline>
<reference_mark>
{{
    "1. Introduction": ["65f8f1a913fb2c6cf6673bae", "6449e7fb582c1376bbfc5c50"],
    "2. Theoretical Foundation": ["647572d8d68f896efa7b72fe", "624e569e5aee126c0f7f904c"],
    "3. Core Planning Enhancement Methods": {{
        "3.1 Prompt Engineering Based Methods": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4"],
        "3.2 Architecture Enhancement Methods": ["6528a864939a5f4082579edb", "623004305aee126c0f9b322d", "63dcdb422c26941cf00b62de"],
        "3.3 Training Strategy Based Methods": ["63dcdb422c26941cf00b62de", "63d7352390e50fcafda302af", "65543326939a5f40820ac89a"],
        "3.4 Tool Integration Methods": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4"]
    }},
    "4. Comparative Analysis": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4", "64927546d68f896efa88a3a2"],
    "5. Application Scenarios": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4"],
    "6. Challenges and Future Directions": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4", "64927546d68f896efa88a3a2"],
    "7. Conclusion": ["65f8f1a913fb2c6cf6673bae", "616e374a5244ab9dcbd1b2c4"]
}}
</reference_mark>


# 注意事项
1. 你的输出中必须包含<detail_outline>和<reference_mark>两部分内容。
2. 你需要使用到「已有的论文片段」中的尽可能多的论文ID来编写综述。
3. 你需要保证综述全面覆盖了所有关键和外围主题，提供了详细的讨论和广泛的信息。
4. 你需要保证综述结构紧凑，逻辑清晰，各部分内容安排最为合理，相邻部分之间的过渡流畅且无冗余。
5. 你需要保证综述内容高度聚焦且完全切题，文章紧扣主题，每一处信息都为全面理解该主题做出贡献。
6. 不要捏造不存在的论文ID。
7. 每个章节引用的论文ID数量不要少于5个，并且需要覆盖所有提供给你的论文ID。

# 已有的论文片段
{paper_chunks}

"""